library(tidyverse)
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## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.0     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0
## ── Conflicts ──────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(plotly)
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## Attaching package: 'plotly'
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library(gapminder)

Effective Visualizations

Now that you know how to create graphics and visualizations in R, you are armed with powerful tools for scientific computing and analysis. With this power also comes great responsibility. Effective visualizations is an incredibly important aspect of scientific research and communication. There have been several books (see references) written about these principles. In class today we will be going through several case-studies trying to develop some expertise into making effective visualizations.

Worksheet

The worksheet questions for today are embedded into the class notes.

You can download this Rmd file here

Note, there will be very little coding in-class today, but I’ve given you plenty of exercises in the form of a supplemental worksheet (linked at the bottom of this page) to practice with after class is over.

Resources

  1. Fundamentals of Data Visualization by Claus Wilke.

  2. Visualization Analysis and Design by Tamara Munzner.

  3. STAT545.com - Effective Graphics by Jenny Bryan.

  4. ggplot2 book by Hadley Wickam.

  5. Callingbull.org by Carl T. Bergstrom and Jevin West.

Part 1: Warm-up and pre-test [20 mins]

Warmup:

Write some notes here about what “effective visualizations” means to you. Think of elements of good graphics and plots that you have seen - what makes them good or bad? Write 3-5 points.

  1. No distracting colors; only use color for a purpose.
  2. Including values on bar/pie graphs. Showing data transparently.
  3. Faceting to show differences between groups.

CQ01: Weekly hours for full-time employees

Question: Evaluate the strength of the claim based on the data: “German workers are more motivated and work more hours than workers in other EU nations.”

Very strong, strong, weak, very week, do not know - Strong, the graph is powerful for showing this claim, but it doesn’t prove that they are “more motivated” - the graph doesn’t provide a reason WHY Germans work more hours

  • Main takeaway: Need to set bar graph axis to 0 in order to accurately show differences between groups.

CQ02: Average Global Temperature by year

Question: For the years this temperature data is displayed, is there an appreciable increase in temperature?

Do not know because we don’t know how this compares to other chunks of time. There could be some amount of variation in the system that makes that normal. It could be showing one year or thirty years.

  • Main takeaway: Need to make y-axis appropriate to the data in order not to obsure from the data.

CQ03: Gun deaths in Florida

Question: Evaluate the strength of the claim based on the data: “Soon after this legislation was passed, gun deaths sharply declined.”

Very strong, strong, weak, very week, do not know - Very Weak - the x-axis are flipped so the number of gun deaths actually sharply increased.

  • Main takeaway: Be careful with your axes and colors.

Part 2: Extracting insight from visualizations [20 mins]

Great resource for selecting the right plot: https://www.data-to-viz.com/ ; encourage you all to consult it when choosing to visualize data.

Case Study 1: Context matters

Case Study 2: A case for pie charts

Part 3: Principles of effective visualizations [20 mins]

We will be filling these principles in together as a class

  1. Proportional ink
  2. High data:ink ratio
  3. Good axes and titles
  4. Choose colors that don’t distract and can be seen by everybody and printed in b/w
  5. Choose the right graph-type for the data.

Make a great plot worse

Instructions: Here is a code chunk that shows an effective visualization. First, copy this code chunk into a new cell. Then, modify it to purposely make this chart “bad” by breaking the principles of effective visualization above. Your final chart still needs to run/compile and it should still produce a plot.

How many of the principles did you manage to break?

Plotly demo [10 mins]

Did you know that you can make interactive graphs and plots in R using the plotly library? We will show you a demo of what plotly is and why it’s useful, and then you can try converting a static ggplot graph into an interactive plotly graph.

This is a preview of what we’ll be doing in STAT 547 - making dynamic and interactive dashboards using R!

suppressPackageStartupMessages(library(plotly))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(gapminder))
p <- ggplot(gapminder, aes(x=log(gdpPercap), y=lifeExp, color=continent))+
  geom_point()

p %>% 
  ggplotly()
p <- gapminder %>% 
  plot_ly(x = ~gdpPercap,
          y = ~lifeExp,
          color = ~continent,
          type = "scatter",
          mode = "markers")

Sys.setenv("plotly_username"="pennykahn")
Sys.setenv("plotly_api_key"="38ghDxXXAr8nrl6v1PO6")

api_create(p, filename = "cm013_plotly_example")
## Found a grid already named: 'cm013_plotly_example Grid'. Since fileopt='overwrite', I'll try to update it
## Found a plot already named: 'cm013_plotly_example'. Since fileopt='overwrite', I'll try to update it

here

Supplemental worksheet (Optional)

You are highly encouraged to the cm013 supplemental exercises worksheet. It is a great guide that will take you through Scales, Colours, and Themes in ggplot. There is also a short guided activity showing you how to make a ggplot interactive using plotly.